Part, sensor, and metrology data integration

Patent metadata

Field	Value
Publication number	US-2024061409-A1
Application number	US-202318495724-A
Country	US
Kind code	A1
Filing date	Oct 26, 2023
Priority date	Feb 17, 2021
Publication date	Feb 22, 2024
Grant date	—

Abstract

A method includes identifying sets of part data associated with substrate processing equipment. Each of the sets of part data includes corresponding part values and a corresponding part identifier. Each of the sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment. The method further includes generating sets of aggregated data. Each of the sets of aggregated data includes a corresponding set of part data of the sets of part data and a corresponding set of additional non-part data of sets of non-part data. The method further includes causing, based on the sets of aggregated data, performance of a corrective action associated with the substrate processing equipment.

First claim

What is claimed is:

1 . A method comprising:
identifying a plurality of sets of part data associated with substrate processing equipment, wherein each of the plurality of sets of part data comprises corresponding part values and a corresponding part identifier, and wherein each of the plurality of sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment; generating a plurality of sets of aggregated data, each of the plurality of sets of aggregated data comprising a corresponding set of part data of the plurality of sets of part data and a corresponding set of additional non-part data of a plurality of sets of non-part data; and causing, based on the plurality of sets of aggregated data, performance of a corrective action associated with the substrate processing equipment.

2 . The method of claim 1 , wherein the plurality of sets of non-part data comprise a plurality of sets of sensor data, and wherein each of the plurality of sets of sensor data comprises corresponding sensor values associated with producing one or more corresponding substrates by the substrate processing equipment and a corresponding sensor data identifier.

3 . The method of claim 2 , wherein each of the plurality of sets of sensor data is associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce the one or more corresponding substrates.

4 . The method of claim 1 , wherein the plurality of sets of non-part data comprise a plurality of sets of metrology data, and wherein each of the plurality of sets of metrology data comprises corresponding metrology values associated with one or more corresponding substrates and a corresponding metrology data identifier.

5 . The method of claim 4 , wherein each of the plurality of sets of metrology data is associated with the one or more corresponding substrates produced by one or more corresponding substrate processing operations performed by the substrate processing equipment that comprises the corresponding equipment part.

6 . The method of claim 1 , wherein the plurality of sets of part data comprise one or more of part manufacturing data, part measurement data, or part material property data.

7 . The method of claim 1 , wherein at least a portion of the plurality of sets of part data are measured via one or more of automated optical inspection (AOI) equipment, an atomic force microscope (AFM), or a coordinate measurement (CMM) machine.

8 . The method of claim 1 , wherein the generating of the plurality of sets of aggregated data comprises:
determining common portions between each corresponding part identifier and each additional non-part identifier to identify matches; and for each of the matches, generating a corresponding set of aggregated data that comprises a respective set of part data that corresponds to the corresponding part identifier and a respective set of additional non-part data that corresponds to the corresponding additional non-part identifier to generate the plurality of sets of aggregated data.

9 . The method of claim 1 , wherein the causing of the performance of the corrective action comprises training a machine learning model using the plurality of sets of aggregated data.

10 . The method of claim 1 , wherein the causing of the performance of the corrective action comprises providing the plurality of sets of aggregated data to a trained machine learning model and receiving, from the trained machine learning model, one or more outputs to perform the corrective action.

11 . The method of claim 1 , wherein the causing of the performance of the corrective action comprises storing the plurality of sets of aggregated data to train a machine learning model to provide a trained machine learning model, and wherein the trained machine learning model is capable of generating one or more outputs to perform the corrective action.

12 . The method of claim 1 , wherein the performance of the corrective action comprises one or more of:
updating design of the corresponding equipment part; updating quality of the corresponding equipment part; updating dimensions of the corresponding equipment part; updating feature layout of the corresponding equipment part; updating part manufacturing operations to produce the corresponding equipment part; or performing root cause analysis to determine updates to the corresponding equipment part or to one or more corresponding substrate processing operations.

13 . A system comprising:
a memory; and a processor, coupled to the memory, to:
identify a plurality of sets of part data associated with substrate processing equipment, wherein each of the plurality of sets of part data comprises corresponding part values and a corresponding part identifier, and wherein each of the plurality of sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment;
generate a plurality of sets of aggregated data, each of the plurality of sets of aggregated data comprising a corresponding set of part data of the plurality of sets of part data and a corresponding set of additional non-part data of a plurality of sets of non-part data; and
cause, based on the plurality of sets of aggregated data, performance of a corrective action associated with the substrate processing equipment.

14 . The system of claim 13 , wherein:
the plurality of sets of non-part data comprise a plurality of sets of sensor data; each of the plurality of sets of sensor data comprises corresponding sensor values associated with producing one or more corresponding substrates by the substrate processing equipment and a corresponding sensor data identifier; and each of the plurality of sets of sensor data is associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce the one or more corresponding substrates.

15 . The system of claim 13 , wherein:
the plurality of sets of non-part data comprise a plurality of sets of metrology data; each of the plurality of sets of metrology data comprises corresponding metrology values associated with one or more corresponding substrates and a corresponding metrology data identifier; and each of the plurality of sets of metrology data is associated with the one or more corresponding substrates produced by one or more corresponding substrate processing operations performed by the substrate processing equipment that comprises the corresponding equipment part.

16 . The system of claim 13 , wherein the plurality of sets of part data comprise one or more of part manufacturing data, part measurement data, or part material property data.

17 . The system of claim 13 , wherein at least a portion of the plurality of sets of part data are measured via one or more of automated optical inspection (AOI) equipment, an atomic force microscope (AFM), or a coordinate measurement (CMM) machine.

18 . A non-transitory computer readable medium having instructions stored thereon, which, when executed by a processing device, cause the processing device to:
identify a plurality of sets of part data associated with substrate processing equipment, wherein each of the plurality of sets of part data comprises corresponding part values and a corresponding part identifier, and wherein each of the plurality of sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment;

Assignees

• Applied Materials Inc

Inventors

• Sin Garrett Ho-Yee
• Bhatia Sidharth
• Guyomard Katty Marie Lydia Gamon
• Jafari Shawyon
• Pai Heng-Cheng
• Nambiar Pramod
• Brillhart Paul Lukas
• Durukan Ilker

Classifications

• G01N33/0095

  Semiconductive materials · CPC title

• G05B19/41875 Primary

  characterised by quality surveillance of production · CPC title

• G06N20/00

  Machine learning · CPC title

• G01N33/00 Primary

  Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00 · CPC title

• G06F30/398

  Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] (optical proximity correction [OPC] design processes G03F1/36) · CPC title